An empirical study of today's Internet traffic for differentiated services IP QoS
Bibliographic record
Abstract
The IETF is currently focused on differentiated services (Diffserv) as the architecture to provide quality of service in IP networks (IP-QoS). The Diffserv architecture consists of two key components: traffic conditioning at the edge and per hop behaviour (PHB) at the core. Traffic conditioners are realized through various building blocks such as classifier, meter, marker, policer, etc. This paper focuses on an empirical study of pre-Diffserv Internet traffic. We use the results of the study to understand design choices for some of the Diffserv architecture building blocks. The primary contribution is an investigation into the usage of the type-of-service (TOS) field marking to understand if its usage justifies the IETF Diffserv Working Groups (WGs) standardization of class selector PHB on the basis of backwards compatibility with the TOS octet. A second contribution is a detailed examination of Internet and intranet traffic traces to understand issues related to packet classification in Diffserv edge routers. In particular, we study the need for classifiers that maintain per-flow state and utilize techniques based on inspection of layer-7 header/payload.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".